Dynamic Travel Time Estimation for Northeast Illinois Expressways

Having access to accurate travel time is critical for both highway network users and traffic operators. Travel time that is currently reported for most highways is estimated by employing naïve methods that use limited sources of data. This might result in inaccurate travel time prediction and could impose difficulties on travelers. The purpose of this report is to develop an enhanced travel time prediction model using multiple data sources, including loop detectors, probe vehicles, weather condition, geometry, roadway incidents, roadwork, special events, and sun glare. Different models are trained accordingly based on machine learning techniques to predict travel time 5 min, 10 min, and even 60 min ahead. A comparison of techniques showed that 15 min or shorter prediction horizons are more accurate when applying the random forest model, although the prediction accuracy of longer prediction horizons is still acceptable. An algorithm is proposed for dynamic prediction of travel time in which the travel time of each highway corridor is calculated by adding the predicted travel time of each link of the corridor. The proposed dynamic approach is tested and evaluated on highways and showed a significant improvement in the accuracy of predicted travel time in comparison to the snapshot travel time prediction approach. Traffic-related variables, especially occupancy, are found to be effective in short-term travel time prediction using loop-detector data. This suggests that among traffic variables collected by loop detectors, occupancy can capture traffic condition better than other variables. Fusion of several data sources, however, increases prediction accuracy of the models.IDOT-R27-177Ope

Traffic Modeling and Management, Innovative Data and Methods

In this dissertation, the main objective is to propose innovative data and methods to model and improve traffic operation and management in three directions: detecting and analyzing the occurrence of accidents in highways in real-time, improving the operation of reversible lanes to reduce congestion, and addressing the impact of connected and autonomous vehicle (CAV) on daily traffic. Therefore, first, an Extreme Gradient Boosting (XGBoost) model is trained to detect the occurrence of accidents and SHAP method is employed to analyze generated features from traffic, network, demographic, land use, and weather data sources. Second, to reduce congestion of highways, integration of real-time data-driven techniques and an offline statistical approach is proposed to optimize the operation of reversible express lanes. To this end, different delay indices are generated to measure and improve the performance of reversible lanes in passing the traffic more efficiently. Third, to address the impact of CAV on daily traffic, the current study presents data-driven techniques to relate changes in network traffic flows resulted by implementing CAV technology to traffic network and built-environment characteristics. Therefore, a comprehensive set of features representing network characteristics and urban structure patterns are generated and different machine learning techniques are used to characterize changes in Average Daily Traffic (ADT) under implementation of CAV scenario

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time.</p

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time

Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway

Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.IDOT-ICT-195Ope

A Data-Driven Approach to Characterize the Impact of Connected and Autonomous Vehicles on Traffic Flow

The current study aims to present a model to characterize changes in network traffic flows as a result of implementing connected and autonomous vehicle (CAV) technology based on traffic network and built-environment characteristics. To develop such a model, first, POLARIS agent-based modeling platform is used to predict changes in average daily traffic (ADT) under CAVs scenario in the road network of Chicago metropolitan area as the dependent variable of the model. Second, a comprehensive set of variables and indicators representing network characteristics and urban structure patterns are generated. Three machine learning models namely K-Nearest neighbors, Random Forest, and eXtreme Gradient Boosting are developed and validated to establish the relationship between network characteristics and changes in ADT under CAVs scenario. The estimated models are found to yield acceptable performance. In addition, SHapley Additive exPlanations (SHAP) analysis tool is employed to investigate the impact of important features on changes in ADT, which discloses the most important link properties, network features, and demographic information in predicting change in ADT under the analyzed CAVs scenario